Method and system for determining in-band optical noise

ABSTRACT

There is provided a method for determining the in-band noise in agile multichannel Dense Wavelength Division Multiplexing (DWDM) optical systems, where the interchannel noise is not representative of the in-band noise in the optical channel. The method relies on the analysis of two observations of the same input optical signal. In the two observations, the linear relationship between the optical signal contribution and the optical noise contribution (e.g. the observed OSNR) is different, which allows the discrimination of the signal and noise contributions in the input optical signal. In a first approach, the two observations are provided by polarization analysis of the input optical signal. In a second, the input optical signal is obtained using two different integration widths.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/594,503, now pending, which is a national stage entry ofPCT/CA2008/000647 filed Apr. 4, 2008, the specification of which beinghereby incorporated by reference. This application claims priority ofU.S. provisional patent application No. 60/910,352 filed Apr. 5, 2007,the specification of which being hereby incorporated by reference.

TECHNICAL FIELD

The invention relates to the determination of the in-band noise inoptical telecommunication applications. More specifically, the inventionrelates to the determination of the in-band noise in Dense WavelengthDivision Multiplexing (DWDM) optical networks.

BACKGROUND

The IEC 61280-2-9 Fiber-optic communication subsystem testprocedures—Part 2-9 standards (ed. 1.0 b:2002) provides a standardmethod for determining OSNR in DWDM networks. This method is based onthe assumption that the interchannel noise level is representative ofthe noise level at the signal peak position. The method interpolates thepower level of the noise outside the signal bandwidth to evaluate thein-band noise in the signal bandwidth. Increased modulation rates, whichenlarge the signal bandwidth, and increased channel density reduce theinterchannel width, therefore resulting in severe spectralcharacteristics requirements for the optical spectrum analyzers used toperform the measurement. The procedures described in the standards areable to cope with these difficulties when the noise level of adjacentpeaks is mostly continuous. For example, the standards propose atwo-scan procedure to first measure a broad modulated peak with a largerresolution bandwidth to capture the entire signal peak and thendetermine the noise using a narrow resolution bandwidth to minimize thecontributions of the main and adjacent peaks on the interchannel noiselevel. Alternatively, commercial Optical Spectrum Analyzers (OSA) (suchas EXFO's FTB-5240, in its versions available before 2007) implement asomewhat equivalent procedure by performing an integrated peakcalculation and fine noise determination in a single scan.

However, to strictly comply with the standards recommendation, the noiselevel should be determined at the mid-channel spacing between peaks. Inthe case where noise is spectrally filtered with the signal peak, afterpassing through multiplexers or demultiplexers—such as ReconfigurableOptical Add Drop Multiplexers (ROADM)—the mid-spacing noise level is nolonger representative of the in-band noise level, which is the relevantparameter for the OSNR determination. The interpolation of theinterchannel noise level becomes unreliable. This can be mitigated byrelying on a very sharp spectral response of the OSA filter and adaptiveprocessing to determine the noise level at the shoulders where the noisemeets the base of a signal peak within the channel bandwidth. However,increased modulation rates combined with narrow filtering ofmultiplexers and demultiplexers is making it increasingly difficult toachieve a reliable measurement of the noise level within the channelbandwidth.

Active polarization nulling (see J. H. Lee et al., “OSNR MonitoringTechnique Using Polarization-Nulling Method”, IEEE Photonics TechnologyLetters, Vol. 13, No. 1, January 2001) provides an alternative to adirect analysis of the optical spectrum. This method uses the fact thatthe signal peak is generally polarized while the noise is generallyunpolarized. Using a polarization controller cascaded with a polarizer,it is possible to actively control the polarization of the input signalin order to find a condition where the signal peak is substantiallysuppressed by the polarizer. An optical spectrum trace is acquired whilethe signal peak is suppressed and reveals the in-band noise within theoptical channel bandwidth, discriminated from the signal peak by thesignal peak suppression. The noise level within the optical channelbandwidth can be determined using the acquired optical spectrum trace.

Other methods for determining the OSNR of an optical signal have alsobeen proposed. One of them is disclosed in U.S. Patent Application Pub.No. 2006/0098980 to Lee et al. This method uses a dithering function formeasuring the noise and the signal simultaneously using a differentresolution bandwidth and in a single measurement step, as opposed to theIEC method where the measurement is done in two optical spectrum scans.

SUMMARY

There is provided a method for determining a noise parameter, such asthe in-band noise or the Optical Signal-to-Noise Ratio (OSNR), of aDense Wavelength Division Multiplexing (DWDM) input optical signalhaving a signal and a noise contribution within an optical signalbandwidth. The method can be implemented with only passive opticaldevices and a commercially available optical spectrum analyzer (OSA), asopposed to actively controlled polarization controllers. At least twooptical spectrum traces of the input optical signal obtained by the OSAare processed to discriminate the noise contribution from the signalcontribution.

The method is particularly valuable for determining the in-band noiseand thus the OSNR in agile multichannel Dense Wavelength DivisionMultiplexing (DWDM) optical systems. In such agile systems, opticalchannels may be added or dropped anywhere along an optical network,after or before being optically amplified. Adding and dropping istypically performed using Optical Add Drop Multiplexers (OADM) which notonly filter the signal peak corresponding to the optical channel butalso filter the noise. The optical noise is filtered with the usefulsignal peak and is consequently spectrally limited to the channelbandwidth or spectral neighborhood of the optical channel and alsovaries from one DWDM channel to another. The interchannel noise istherefore not representative of the in-band noise of the opticalchannel.

The method relies on the analysis of at least two measurements of thesame input optical signal which has a useful signal contribution(corresponding to the signal peak) and a noise contribution. In the twomeasurements, the linear relationship between the signal contributionand the noise contribution, i.e. the observed OSNR, is different whichallows the discrimination of the signal and noise contributions in theinput optical signal using calculations and a comparison between the twomeasurements. In a first approach presented herein and which will bereferred to as the Passive Polarization-Induced Discrimination (PPID)approach, the two measurements are provided by different polarizationanalysis of the input optical signal. At least two samples of the inputoptical signal are taken under different polarization analysisconditions, e.g. polarized according to a different state ofpolarization (SOP), the optical spectrum of which being acquired toprovide the at least two measurements. In one example of this approach,the input optical signal is split using a polarization beam splitter andthe two split samples are acquired to provide the two measurements. In asecond approach presented herein and referred to as the DifferentialResolution Bandwidth Discrimination (DRBD) approach, the input opticalsignal is acquired using two different Resolution Bandwidths (RBW's).The different RBW's may be provided using a different filtering slit inthe Optical Spectrum Analyzer (OSA) or a single acquired input trace mayby integrated numerically to provide the two measurements.

The PPID and the DRBD approaches have some different advantages anddrawbacks and can be combined into a hybrid approach where limitationsof one are circumvented by using the other.

The method relies on the differential properties of the noise and signalcontributions in the input optical signal to be analyzed. Firstly, thesignal and noise contributions have different properties in that thesignal is typically substantially polarized (or at least partiallypolarized) where the noise is typically unpolarized (or at leastpartially unpolarized). The PPID approach takes advantage of thischaracteristic. Accordingly, the insertion of a polarizer in the opticalpath of the input optical signal will have a different effect on thenoise contribution than on the signal contribution. It is thus possibleto provide different optical spectrum traces with different proportionsof signal and noise contributions, which allows for their discriminationby assuming that the noise contribution is mostly unpolarized while thesignal contribution is mostly polarized. Secondly, the noisecontribution is typically spectrally broader than the signalcontribution, and more importantly, it varies slowly where the signalvaries quickly as compared to the resolution bandwidth of the OSA. TheDRBD approach takes advantage of this second characteristic.Accordingly, varying the RBW in different optical spectrum traces of theinput optical signal will have a different effect on the noisecontribution than on the signal contribution. It is thus possible toprovide different optical spectrum traces with different proportions ofsignal and noise contributions, which allows for their discrimination.

According to one aspect, there is provided a method for determining anoise parameter on an input optical signal having a data-carrying signalcontribution and a noise contribution within an optical signalbandwidth. The method comprises the steps of : obtaining at least twooptical spectrum traces from the input optical signal, the opticalspectrum traces being taken under different conditions such that theyshow different non-zero signal-to-noise ratios; mathematicallydiscriminating the signal contribution from the noise contributionwithin the optical signal bandwidth using the optical spectrum traces;determining an in-band noise level on the input optical signal from thediscriminated noise contribution; and determining the noise parameterfrom the determined in-band noise level, the noise parameter beingindicative of the noise contribution within the optical signalbandwidth.

According to another aspect, there is provided a method for determininga noise parameter on an input optical signal having a data-carryingsignal contribution and a noise contribution within an optical signalbandwidth, the signal contribution being mostly polarized and the noisecontribution being mostly unpolarized. The method comprises: acquiring afirst and a second optical spectrum trace of the input optical signalcorresponding to respective first and second polarization analysisconditions, the first and second polarization analysis conditions beingmutually different and arbitrary relative to the input optical signalsuch that the optical spectrum traces show mutually different non-zerosignal-to-noise ratios; mathematically discriminating the signalcontribution from the noise contribution within the optical signalbandwidth based on the optical spectrum traces; and determining thenoise parameter from the discriminated noise contribution, the noiseparameter being indicative of the noise contribution within the opticalsignal bandwidth.

According to another aspect, there is provided a system for determininga noise parameter on an input optical signal within an optical signalbandwidth. The system comprises: an input for receiving the inputoptical signal comprising a data-carrying signal contribution and anoise contribution within the optical signal bandwidth, the signalcontribution and the noise contribution having mutually differentdegrees of polarization; a polarization optics arrangement for obtaininga first and a second sample of the input optical signal under mutuallydifferent polarization analysis conditions such that at least one of thefirst and the second sample is polarization analyzed, a state ofpolarization of the at least one polarization analyzed sample beingarbitrary relative to a state of polarization of the input opticalsignal; an optical spectrum analyzer for acquiring a first and a secondoptical spectrum trace respectively of the first and second samples, thefirst and second optical spectrum traces showing mutually differentnon-zero signal to noise ratios; a spectrum processor adapted formathematically discriminating the noise contribution in the inputoptical signal within the optical signal bandwidth based on the firstand second optical spectrum traces; and a noise calculator forevaluating the noise parameter within the optical signal bandwidth fromthe discriminated noise contribution.

According to another aspect, there is provided a method for determininga noise parameter on an input optical signal having a data-carryingsignal contribution and a noise contribution within an optical signalbandwidth, the noise contribution varying slowly in wavelength withinthe optical signal bandwidth compared to the signal contribution. Themethod comprises the steps of : obtaining a first and a second opticalspectrum traces from the input optical signal, respectivelycorresponding to a first and a second integration widths in order forthe first and the second optical spectrum traces to show differentnon-zero signal-to-noise ratios, the second integration width beinglarger than the first integration width, the first and the secondoptical spectrum traces comprising at least one point; mathematicallydiscriminating the noise contribution in the input optical signal withinthe optical signal bandwidth using the first and the second opticalspectrum traces; determining, from the discriminated noise contribution,an in-band noise level on the input optical signal within the opticalsignal bandwidth; and determining the noise parameter from thedetermined in-band noise level, the noise parameter being indicative ofthe noise contribution within the optical signal bandwidth.

According to another aspect, there is provided a method for determiningan in-band noise level on an input optical signal having a data-carryingsignal contribution and a noise contribution within an optical signalbandwidth, wherein an optical spectrum trace is obtained from the inputoptical signal, and wherein the noise contribution is discriminated fromthe signal contribution within the optical signal bandwidth in order todetermine an in-band noise level on the input optical signal. The methodis characterized in that : at least two optical spectrum traces areobtained from the input optical signal, the optical spectrum tracesbeing taken under different conditions such that they show differentnon-zero signal to noise ratios; the noise contribution is discriminatedfrom the signal contribution within the optical signal bandwidth using acomparison between the optical spectrum traces; and the in-band noiselevel on the input optical signal is determined from the discriminatednoise contribution.

According to another aspect, there is provided a method for determiningan in-band noise level on an input optical signal having a data-carryingsignal contribution and a noise contribution within an optical signalbandwidth, wherein the signal contribution and the noise contributionhave at least one of different degrees of polarization and differentstates of polarization from one another, wherein a first opticalspectrum trace of the input optical signal is acquired using a firstpolarization analysis condition, and wherein the noise contribution isdiscriminated from the signal contribution within the optical signalbandwidth in order to determine an in-band noise level on the inputoptical signal. The method is characterized by the steps of : acquiringa second optical spectrum trace of the input optical signal using asecond polarization analysis condition, the first and secondpolarization analysis conditions being different from one another andeach being arbitrary relative to the input optical signal, the opticalspectrum traces showing different signal to noise ratios;

mathematically discriminating the noise contribution from the signalcontribution within the optical signal bandwidth using a comparisonbetween the optical spectrum traces; and determining the in-band noiselevel on the input optical signal from the discriminated noisecontribution.

According to another aspect, there is provided a system for determiningthe in-band noise level on an input optical signal having adata-carrying signal contribution and a noise contribution within anoptical signal bandwidth, wherein the signal contribution and the noisecontribution have at least one of different degrees of polarization anddifferent states of polarization from one another, the system comprisinga polarization optics arrangement for polarizing at least part of theinput optical signal in order to produce a first sample of the inputoptical signal, an optical spectrum analyzer configured for acquiring afirst optical spectrum trace of the first sample, the noise contributionbeing discriminated from the signal contribution within the opticalsignal bandwidth in order to determine an in-band noise level on theinput optical signal. The system is characterized in that : the firstoptical spectrum trace shows a non-zero signal to noise ratio; thepolarization optics arrangement is further arranged to produce a secondsample of the input optical signal, the first and the second sampleshaving states of polarization relative to the input optical signaldifferent from one another or degrees of polarization different from oneanother, states of polarization of the first and the second samplesbeing arbitrary relative to the input optical signal; the opticalspectrum analyzer is further configured for acquiring a second opticalspectrum trace of the second sample, the second optical spectrum traceshowing a non-zero signal to noise ratio different from the one of thefirst optical spectrum trace; the system also comprises a spectrumprocessor for mathematically discriminating the noise contribution fromthe signal contribution within the optical signal bandwidth based on thefirst and the second optical spectrum traces; and a noise calculator fordetermining the in-band noise within the optical signal bandwidth fromthe discriminated noise contribution.

According to another aspect, there is provided a method for estimatingan in-band noise level on an input optical signal having a data-carryingsignal contribution and a noise contribution within an optical signalbandwidth, wherein the noise contribution varies slowly in wavelengthwithin the optical signal bandwidth compared to the signal contribution,and wherein a first and a second optical spectrum traces are obtainedfrom the input optical signal respectively corresponding to a first anda second integration widths, the second integration width being largerthan the first integration width. The method is characterized in that :the first and the second optical spectrum traces show different non-zerosignal to noise ratios due to the different integration widths; thenoise contribution is discriminated from the signal contribution withinthe optical signal bandwidth using numerical calculations based on thethe first and the second optical spectrum traces; and the in-band noiselevel on the input optical signal is determined from the discriminatednoise contribution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph illustrating the optical spectrum of an example inputoptical signal along with the optical spectrum of its noise contributionand of its signal contribution;

FIG. 2 is a flow chart illustrating a general method for determining thein-band noise, the Optical Signal to Noise Ratio (OSNR) or another noiseparameter on an input optical signal;

FIG. 3 is a block diagram of the main components of a system fordetermining a noise parameter on an input optical signal using a passivepolarization-induced discrimination method;

FIG. 4A is a block diagram showing a possible polarization opticsarrangements to be used in the system of FIG. 3, wherein a polarizationbeam splitter is used;

FIG. 4B is a block diagram showing another possible polarization opticsarrangements to be used in the system of FIG. 3, wherein a 50/50 opticalcoupler is used along with a polarizer;

FIG. 4C is a block diagram showing another possible polarization opticsarrangements to be used in the system of FIG. 3, wherein a polarizer isalternately inserted and removed from the optical path;

FIG. 4D is a block diagram showing another possible polarization opticsarrangements to be used in the system of FIG. 3, wherein a polarizationcontroller is placed before a polarizer;

FIG. 5 is a flow chart illustrating a method for determining noiseparameter on an input optical signal using a passivepolarization-induced discrimination approach;

FIG. 6 is a block diagram of an embodiment of a system for determining anoise parameter on an input optical signal using a passivepolarization-induced discrimination method, wherein a dual channeloptical spectrum analyzer is used;

FIG. 7 is a flow chart illustrating an example embodiment of the methodof FIG. 5, wherein the step of discriminating the signal and noisecontributions is shown in more details;

FIG. 8 is a graph illustrating the optical spectrum of an example inputoptical signal along with its discriminated noise and signalcontributions, as calculated with a passive polarization-induceddiscrimination method;

FIG. 9 is a flow chart illustrating a method for determining noiseparameter on an input optical signal using a differential resolutionbandwidth discrimination approach;

FIG. 10 is a flow chart illustrating an example embodiment of the methodof FIG. 9 with chosen integrating windows to achieve a flat-top signalpeak on two optical spectrum traces;

FIG. 11 is a graph illustrating the optical spectrum of an example inputoptical signal to which the method of FIG. 10 is applied;

FIG. 12 is a flow chart illustrating another example embodiment of themethod of FIG. 9 assuming a known shape of the signal contribution; and

FIG. 13 is a flow chart illustrating an example method for determiningnoise parameter on an input optical signal using a hybrid approach.

It will be noted that throughout the appended drawings, like featuresare identified by like reference numerals.

DETAILED DESCRIPTION

Now referring to FIG. 1, the methods and systems described herein relateto the characterization of an optical signal p which is used in opticaltelecommunications to transmit data over a Dense Wavelength DivisionMultiplexing (DWDM) optical channel. Throughout the present description,the optical signal p corresponds to one of the DWDM optical channels. Inthe optical channel bandwidth of interest, the optical signal p includestwo components, i.e. a signal contribution s arising from thedata-carrying signal, and a noise contribution n which includes allother sources of optical power within the optical channel. The noisecontribution n arises mostly from the Amplified Spontaneous Emission(ASE) noise of the optical amplifiers in the optical transmissionsystem. FIG. 1 shows the optical spectrum p(λ) of an example opticalsignal p, along with the optical spectrum of its signal contributions(λ) and the optical spectrum of its noise contribution n(λ), such that:

p(λ)=s(λ)+n(λ),

and

p=∫ _(CBW) p(λ),

s=∫ _(CBW) s(λ),

n=∫ _(CBW) n(λ),   (1)

and where CBW is the Channel BandWidth of interest.

An optical spectrum trace of the optical signal p is acquired by anOptical Spectrum Analyzer (OSA) as a series of data pairs P(λ) andrepresents the input optical signal p convolved with the filter spectralresponse of the OSA h_(OSA)(λ) combined with any desired convolutionwindow h_(W)(λ). The optical spectrum trace P(λ) is thus thespectrally-resolved optical power of the optical signal p. In abandwidth corresponding to the channel bandwidth CBW, the opticalspectrum trace P(λ) also includes a signal contribution S(λ) and a noisecontribution N(λ) which are merged together and appear as the opticalspectrum trace P(λ), and:

$\begin{matrix}\begin{matrix}{{P(\lambda)} = {{S(\lambda)} + {N(\lambda)}}} \\{= {{h_{w}(\lambda)}*{h_{OSA}(\lambda)}*{p(\lambda)}}} \\{= {{{h_{w}(\lambda)}*{h_{OSA}(\lambda)}*{s(\lambda)}} + {{h_{w}(\lambda)}*{h_{OSA}(\lambda)}*{{n(\lambda)}.}}}}\end{matrix} & (2)\end{matrix}$

where h_(w)(λ) is a convolution window which may be applied numericallyand “*” denotes the convolution operation.

The methods and systems described herein are used to discriminate thesignal contribution s(λ) from the noise contribution n(λ) in the opticalspectrum p(λ) using acquired optical spectrum traces P(λ) in order todetermine the in-band noise on the optical signal to be characterized.The instrument noise associated with the detection system itself, namelythe OSA, on the acquired optical spectrum trace P(λ) is considered tohave a negligible effect compared to the optical noise contribution tobe characterized.

FIG. 1 shows a single optical signal p within its corresponding opticalchannel but it should be noted that according to wavelength divisionmultiplexing a plurality of optical channels shares the opticalspectrum, each channel for transmitting one optical signal (not shown).It should however be kept in mind that other optical signals aretypically present in the optical spectrum, spectrally on both sides ofthe optical signal p.

A DWDM optical channel is being defined as a spectral bandwidth, i.e.the channel bandwidth, allocated for the transmission of an opticalsignal in a WDM transmission scheme. The signal bandwidth is rather theactual width of the signal peak, i.e. the bandwidth over which thesignal contribution is non negligible. The channel bandwidth CBW may belarger than or just as large as (over even narrower than) the signalbandwidth, depending on the density of the DWDM channels and the signaltransmission rate for a given transmission scheme.

The methods disclosed herein rely on the fact that the properties of thesignal and noise contributions within the optical channel are different.First, the signal s and noise n contributions have differentpolarization properties. The signal contribution s is substantiallypolarized (or at least partially polarized) while the noise contributionn is mostly unpolarized (or at least partially unpolarized). Second, thenoise contribution n is spectrally broader and varies slowly compared tothe signal contribution p in a spectral slice corresponding to at leastthe Resolution BandWidth (RBW) defined by the filter spectral responseof the OSA. It is possible to use at least one of these differentproperties to discriminate the signals from the noise contribution n inacquired optical spectrum traces P(λ).

FIG. 2 illustrates the general approach for determining the in-bandnoise, the Optical Signal to Noise Ratio (OSNR) or another noiseparameter on the input optical signal p.

In step 202, at least two optical spectrum trace measurements P_(A)(λ)and P_(B)(λ) are obtained from the input optical signal p to becharacterized. The two traces P_(A)(λ), P_(B)(λ) are taken on twodifferent conditions such that the linear relationship between thesignal contribution and the noise contribution, i.e. the observed OSNR,is different on both traces. This allows for the discrimination of thesignal and noise contributions in the input optical signal p. As will beexplained in more detail when describing the approaches below, thedifference between the two traces P_(A)(λ), P_(B)(λ) arises from thedifference in the optical properties of the signal and the noisecontributions, combined with the different conditions on which theoptical spectrum traces P_(A)(λ) and P_(B)(λ) are obtained.

In step 204, the signal contribution S and the noise contribution N arediscriminated from one another in the optical bandwidth of interestusing calculations and a comparison between said optical spectrum tracesP_(A)(λ) and P_(B)(λ). Examples of such calculations that allow signaland noise discrimination are given herein below. Such calculations takeinto account the difference in properties between the signal and thenoise contributions in order to eliminate one or the other in acombination or comparison between the obtained optical spectrum tracesP_(A)(λ) and P_(B)(λ). It is noted that more than two traces could alsobe obtained and used in these calculations.

In step 206, the in-band noise level N(λ_(p)) under the signal peak isdetermined from the discriminated noise contribution N. Again, exampleembodiments for this step are described in more details herein below.

In step 208, the noise parameter is determined, by calculations, fromthe determined in-band noise N(λ_(p)). The noise parameter is indicativeof the noise contribution n within the optical signal bandwidth SB, suchas the determined in-band noise level N(λ_(p)) itself or the OSNRcalculated from the determined in-band noise level. The noise parameteris typically output for use in monitoring, installation, commission,maintenance or troubleshooting of a DWDM optical system. It can begraphically or numerically output using a display unit or a printer forexample. It can also be output by generating an electrical signal or bystoring it in memory for later retrieval.

Two different approaches for determining the in-band noise or the OSNRof an input optical signal p in a DWDM optical system are provided. Thefirst approach will be referred to as the Passive Polarization-InducedDiscrimination approach (PPID) and the second as the DifferentialResolution Bandwidth Discrimination (DRBD) approach. The two approacheshave some different advantages and drawbacks and can be combined into ahybrid approach where deficiencies of one can be circumvented by usingthe other.

Passive Polarization-Induced Discrimination Approach (PPID)

Referring to FIG. 1, let p(λ) be the optical spectrum of the inputoptical signal p and having a signal contribution s(λ) and a noisecontribution n(λ).

The PPID approach uses the differential properties between the signalcontribution s(λ) and the noise contribution n(λ) in the input opticalsignal to be analyzed. The signal s(λ) and noise n(λ) contributions havedifferent polarization properties in that the signal is typicallysubstantially polarized, but at least partially polarized, where thenoise is typically unpolarized, or at least partially unpolarized. Inother words, the signal and the noise contributions have differentdegrees of polarization from one another. This last condition will beassumed for the following description. It is noted, however, that asimilar method could also be used if the signal and noise contributionsrather had different states of polarization from one another.

A system 10 for determining a noise parameter on the input opticalsignal p is shown on FIG. 3. The system receives the input opticalsignal p to be characterized. Due to the different polarizationproperties of the signal s and the noise n contribution, the insertionof a polarization optics arrangement 14 in the optical path of the inputoptical signal p has a different effect on the noise contribution n thanon the signal contribution s. The polarization optics arrangement 14 isused to obtain two different samples p_(A) and p_(B) of the inputoptical signal p by applying two different polarization analysisconditions. Different possible polarization optics arrangements 14 areshown in FIGS. 4A-4D and are described below.

An OSA 11 acquires the optical spectrum traces P_(A)(λ) and P_(B)(λ)respectively of the two samples p_(A) and p_(B). As a consequence of thedifferent polarization analysis conditions between the two samples p_(A)and p_(B), the acquired traces P_(A)(λ) and P_(B)(λ) show differentOSNRs. It is noted that in the special case where the OSNR is null onone of the acquired traces, i.e. the signal is completely suppressed,the following method is also applicable.

A spectrum processor 18 receives the two traces P_(A)(λ), P_(B)(λ) anddiscriminates the noise contribution n and the signal contribution s. Aswill be described hereinbelow, the discrimination is typically performedby subtracting the traces from one another to remove the noisecontribution and provide a difference optical spectrum substantiallyproportional to the spectrum of the signal contribution and from whichthe optical spectrum of the signal S(λ), and thus the optical spectrumof the noise N(λ) can be estimated. It should be noted that a linearprocessing, such as filtering, linear transformation into anotherdomain, etc., can be applied to the original traces P_(A)(λ), P_(B)(λ)before applying the herein presented processing. A noise calculator 20evaluates the in-band noise N(λ_(p)) from the discriminated opticalnoise N(λ). The OSNR is then optionally calculated by an OSNR calculator22 using the in-band noise N(λ_(p)) and the discriminated signal S(λ).

FIGS. 4A to 4D show different possible polarization optics arrangements14 that can be used. In the example arrangement depicted in FIG. 4A, apolarization beam splitter 402 splits the input optical signal p intotwo orthogonal polarizations PA and p_(B). The two samples p_(A) andp_(B) consequently correspond to different polarization analysisconditions. Acquisition of the two samples can then be made using a dualchannel OSA or an optical switch can be used in front of a singlechannel OSA for alternating between the two polarization analysisconditions. It is also noted that the polarization splitter can beplaced before or after the monochromator of the OSA.

The example arrangement depicted FIG. 4B comprises a 50/50 opticalcoupler 404 that splits the input optical signal p onto two paths A andB. A polarizer 406 is inserted on path A to polarize the part of theinput optical signal p propagating on path A in order to provide thefirst sample p_(A), while the part of the input optical signal ppropagating on path B is analyzed without applying a polarization.

The example arrangement depicted FIG. 4C comprises a polarizer 408 thatis alternately inserted in the optical path of the input optical signalp to provide a first sample p_(A), and removed from the path to providethe second sample p_(B). The alternate insertion and removal of thepolarizer 408 from the optical path is controlled by a control unit 410.In FIGS. 4B and 4C, the samples p_(A) and p_(B) correspond to differentpolarization analysis conditions.

The example arrangement depicted FIG. 4D comprises a polarizationcontroller 412 placed before a polarizer 414. The polarizationcontroller 412 is controlled by a control unit 416 which commands avariation of the polarization analysis conditions between theacquisition of sample p_(A) and the acquisition of sample p_(B).

It is also noted that although two paths are shown on FIG. 3 for p_(A)and p_(B) at the output of the polarization optics arrangement 14, whenthe arrangement of FIG. 4C or 4D is used p_(A) and p_(B) are actuallyprovided successively on the same optical path.

In any of the polarization optics arrangement of FIGS. 4A, 4B, 4C and4D, the first and second samples p_(A) and p_(B) of the input opticalsignal p are in different polarization analysis conditions from oneanother, and show different OSNRs. The alignment of the polarizationoptics arrangement to the input optical signal p is arbitrary and it isnot required that the signal contribution be substantially suppressed oneither one of the samples p_(A) and p_(B).

FIG. 5 illustrates a method for determining the in-band noise or theOSNR of the input optical signal p using the PPID approach. In step 502,the two samples p_(A) and p_(B) are produced from the input opticalsignal p using different polarization analysis conditions. The twopolarization analysis conditions and thus the two samples p_(A) andp_(B) are typically produced by the polarization optics arrangement 14.In step 504, the optical spectrum traces P_(A)(λ) and P_(B)(λ),respectively, of the two samples p_(A) and p_(B) are acquired, typicallyusing the OSA 11. In step 506, the noise N and signal S contributionsare discriminated using the acquired traces P_(A)(λ) and P_(B)(λ),typically in the spectrum processor 18. One embodiment of this step isdescribed in more detail below with reference to FIG. 7. In step 508,the in-band noise level N(λ_(p)) is determined from N. This step isperformed, for example, by the in-band noise calculator 20. In step 510,the noise parameter, i.e. the in-band noise or the OSNR, is determinedusing the in-band noise level N(λ_(p)) and is typically output. Thethereby determined noise parameter is output for use in monitoring,installation, commission, maintenance or troubleshooting of a DWDMoptical system. For example, the noise parameter can be output using bygraphical display, by printing, by generating an electrical signal or bystoring it in memory for later retrieval. The in-band noise or OSNR canalso be graphically or numerically output using a display unit or aprinter, along with, for example, the individual and the sum of theacquired spectrum traces (P_(A)(λ), P_(B)(λ), P(λ)). Other parameterscan also be displayed or otherwise output in a graphical or numericalform. The in-band noise level may also be output for optical signalprocessing or for determining the noise figure of an optical amplifierfor example.

FIG. 6 shows one embodiment 10′ of the system 10 generally describedwith reference to FIG. 3. The embodiment 10′ uses a dual-channelpolarization diverse OSA 12 (see, for example, the OSA described in U.S.Pat. No. 6,636,306) to implement the functions of both the polarizationoptics arrangement 14 and the OSA 11. An example of the PPID approachwill be described in more detail below with reference to the system 10′of FIG. 6 and to FIG. 7. The polarization diverse OSA 12 receives theinput optical signal p to be characterized on an input optical fiber 38.The polarization diverse OSA 12 comprises a polarization splitter 34that splits the input optical signal p into two typically orthogonallypolarized samples p_(A) and p_(B), and a dual channel OSA 31. The statesof polarization are arbitrarily aligned with the input optical signal psuch that each polarized samples p_(A), p_(B) typically has a non-zerosignal contribution s_(A), s_(B) and a non-zero noise contributionn_(A), n_(B). The dual channel OSA 31 is used to simultaneously acquirethe optical spectrum traces P_(A)(λ), P_(B)(λ) of two samples p_(A),p_(B). The dual channel OSA 31 comprises a dual channel grating-basedmonochromator 15 receiving both samples p_(A), p_(B) at inputs A and Band separating their wavelength components which are detected using anoptical detector 16 a, 16 b respectively for each channel A, B. Anacquisition unit 17 records the optical spectrum traces P_(A)(λ) andP_(B)(λ) of the two samples p_(A), p_(B). It will be understood that theacquisition is conditioned by the spectral response of the filter of theOSA and that the acquisition step typically comprises applyingcalibration factors, signal conditioning and processing to each detectedtrace.

The PPID approach requires that the OSNR of the two samples p_(A), p_(B)have a non-negligible difference. It may not be the case, for example,if the noise contribution n is substantially unpolarized while at thesame time the signal contribution s is substantially polarized in astate of polarization oriented at 45° relative to the states ofpolarization of the polarization beam splitter 34. When this situationis detected, it can be circumvented by disturbing the input opticalsignal p to provide a small change in its polarization condition. Thispolarization disturbance can be provided manually by moving the inputoptical fiber 38. A polarization disturbance device 36, such as apolarization controller, can be also used to vary the polarizationcondition of the input optical signal p. The optical spectrum tracesP_(A)(λ), P_(B)(λ) corresponding to the two samples p_(A), p_(B) areprocessed by a spectrum processor 18 which compares the acquired tracesP_(A)(λ), P_(B)(λ) to discriminate the signal S contribution S and thenoise contribution N in the acquired optical signals. The discriminatedsignal S and noise contribution N are used by an OSNR calculator 30 todetermine the OSNR. A second iteration may be performed if required aswill be explained below. The system 10′ also has a display unit 32 fordisplaying the determined OSNR along with, for example, the individualand the sum of the acquired spectrum (P_(A)(λ), P_(B)(λ), P(λ)). Otherparameters can also be displayed or otherwise output in a graphical ornumerical form.

EXAMPLE 1

One example embodiment of the processing performed by the spectrumprocessor 18 in order to discriminate the signal S and noise Ncontributions is now described with reference to FIG. 7.

In the following, the optical spectrum traces are analyzed and processedin the spectral neighborhood of each of the signal peaks, or DWDMoptical channels, included in the input optical signal and for which adetermination of the in-band noise level is desired. The signal peaksare identified by standard known implementation techniques such thattheir respective central wavelengths (λ_(p)) are determined. The in-bandnoise level and the OSNR on each signal peak can be estimated using theherein described method without requiring any further optical spectrumtrace acquisition.

In step 502, two samples p_(A) and p_(B) are produced from the inputoptical signal p using different polarization analysis conditions asdescribed above and typically using the polarization optics arrangement34 of FIG. 6 in this case. In step 504, the optical spectrum tracesP_(A)(λ) and P_(B)(λ) of the two samples p_(A) and p_(B) are acquired,typically using the dual-channel OSA 31 of FIG. 6.

Let the optical spectrum traces of the two samples P_(A)(λ) and P_(B)(λ)be respectively described as:

P _(A)(λ)=S _(A)(λ)+N _(A)(λ), and   (3)

P _(B)(λ)=S _(B)(λ)+N _(B)(λ),   (4)

where S_(A) and S_(B) are the signal contributions to the two acquiredoptical spectrum traces within the optical channel and where N_(A) andN_(B) are the noise contributions to the two acquired optical spectrumtraces within the optical bandwidth of interest. The global opticalpower spectrum P(λ), which is representative of the optical signal p, isnot necessarily acquired but can also be obtained by adding the acquiredoptical spectrum traces P_(A)(λ), P_(B)(λ) :

P(λ)=P _(A)(λ)+P _(B)(λ),   (5)

and the polarization diverse OSA 12 is calibrated such that P(λ)corresponds to the optical spectrum of the input optical signal p aswould be acquired by a non-diverse OSA if it was acquired beforeentering the system 10′.

According to the PPID approach, two optical spectrum traces P_(A)(λ),P_(B)(λ) are obtained using a polarization diverse OSA and are comparedin order to discriminate the noise and signal contributions in theacquired traces P_(A)(λ), P_(B)(λ).

As in active polarization nulling methods, the signal contribution isassumed to be at least partially polarized and the noise contribution tobe somewhat unpolarized. The PPID method then provides an evaluation ofthe noise and signal contributions. Within the optical bandwidth ofinterest:

N _(B)(λ)=β·_(A)(λ),   (6)

S _(B)(λ)=k·S _(A)(λ),   (7)

where k is a constant which depends on the polarization alignment of theinput optical signal p to polarization splitter 34 when the acquisitionis performed, and where β is a constant which is typically 1 in allcases if the noise contribution N is unpolarized.

The signal and noise contributions are also in different proportions onthe two traces P_(A)(λ), P_(B)(λ), i.e. the OSNRs are different. It isnoted, however, that the OSNRs are initially unknown and that the signaland noise contributions need to be discriminated before they are known.

The following describes a solution that is derived in the case whereN_(A)(λ)=N_(B)(λ) (or β3 =1), which is the case when the noise isunpolarized. It should however be noted that a special case arises whenthe polarization of the signal contribution s is substantially at 45°from the polarization splitter 34. In this special case, the differencebetween S_(A) and S_(B) is lower than the acquisition resolution of theOSA and k is equal to unity. In that specific case, the OSNR is equal onthe two acquired optical spectrum traces P_(A)(λ) and P_(B)(λ), and thefollowing method cannot be directly used. Accordingly, in step 706, inthe special case where k=1 and β=1, a further acquisition with a variedinput polarization condition (step 708) is performed. For example, instep 708, the input optical fiber 38 is manually moved, or thepolarization disturbance device 36 is used. It is however noted thatwhen k=1 and the noise is different on both traces, i.e.N_(A)(λ)≠N_(B)(λ) (as could be the case if the optical noise is notcompletely unpolarized), a PPID method can still be used withoutrequiring the additional step of disturbing the input optical signal tochange its polarization condition.

According to the assumptions described above, the contribution of thenoise is canceled out in the subtraction of the two optical spectrumtraces P_(A)(λ), P_(B)(λ) which yields:

P _(A)(λ)−P _(B)(λ)=S _(A)(λ)−S _(B)(λ)+(N _(A)(λ)−N_(B)(λ))=(1−k)·S_(A)(λ)   (8)

and

P _(A)(λ)+P _(B)(λ)=S _(A)(λ)+S _(B)(λ)+N _(A)(λ) +N _(B)(λ)=(1+k) ·S_(A)(λ)+N(λ),   (9)

where N(λ)=N_(A)(λ)+N_(B)(λ), and (N_(A)(λ)−N_(B)(λ))=0.

From (8) and (9):

P _(A)(λ)+P _(B)(λ)=[(1+k)/(1−k)]·(P _(A)(λ) −P _(B)(λ))+N(λ)   (10)

S(λ)+N(λ)=K·(P _(A)(λ)−P _(B)(λ))+N(λ),   (11)

S(λ)=K·(P _(A)(λ)−P _(B)(λ)), where K=(1+k)/(1−k).   (12)

K is not known a priori but can be first estimated, in step 710, byassuming that K is a constant which does not vary in wavelength and thatthe noise level is low:

k _(i) =P _(A)(λ_(p))/P _(B)(λ_(P)),   (13)

or letting:

K _(i) =[P _(A)(λ_(P))+P _(B)(λ_(p))][P _(A)(λ_(P))−P _(B)(λ_(p))].  (14)

This is based on the assumption thatK=S_(A)(λ_(p))/S_(B)(λ_(p))=S_(A)/S_(B) which is true provided that thechannel bandwidth CBW over which the analysis is performed is largerthan the optical signal bandwidth or provided that no polarization modedispersion affects the signal, which would thus lead toK(λ)=S_(A)(λ)/S_(B)(λ). In fact, K_(i) is an over estimation that can berelated to K as follows:

K _(i) =K·(1+N(λ_(p))/S(λ_(p))).   (15)

In step 712, from this estimation K_(i), a first estimation of theoptical spectrum of the signal contribution S(λ)_(i) is obtained:

S(λ)_(i) =K _(i)·(P _(A)(λ)−P _(B)(λ))   (16)

In step 714, an estimation of the global optical power spectrum of theinput optical signal is also obtained, namely:

P(λ)=P _(A)(λ)+P _(B)(λ).   (17)

In step 716, a first estimation of the noise contribution N(λ)_(i) isobtained using:

N(λ)_(i) =P(λ)−S(λ)_(i)   (18)

N(λ)_(i)=(P _(A)(λ)+P _(B)(λ))−K _(i)·(P _(A)(λ)−P _(B)(λ)).   (19)

An example of the global optical spectrum P(λ) of an input opticalsignal p is shown in FIG. 8 as well as a first noise estimation N(λ)₀and a further noise estimation N(λ)₁ after one iteration. The othercurves in FIG. 8 represent the acquired traces P_(A)(λ), P_(B)(λ) andthe estimation of the signal contribution S(λ)₀. It can be seen that theerror introduced by K₀ on the first noise estimation N(λ)₀ is large atthe exact position of the signal peak, but becomes minimal on the edgesof the signal peak, i.e. where the edges of the signal estimation meetthe noise estimation. These crossing points c1 and c2 where S(λ)₀crosses N(λ)₀ can be used to provide a first estimation of the in-bandnoise. An interpolation at λ_(p) of the estimated noise obtained atpoints c1 and c2, i.e. N(λ_(c1))₀ and N(λ_(c2))₀, provides an evaluationof the in-band noise N(λ_(p))₀. At these crossing points, the errorintroduced by K₀ on the noise estimation is also 1+N(λ_(p))/S(λ_(p))which, for example, for a reasonable real life S(λ_(p))/N(λ_(p)) of 100(20 dB), contributes for less than 0.05 dB.

Referring back to FIG. 7, in step 718, the in-band noise N(λ_(p))₀ isfirst evaluated using the noise estimation N(λ)₀ at the edges of thesignal peaks, i.e. at the crossing points c1, c2. In step 720, the OSNR₀is calculated using the estimated in-band noise N(λ_(p))₀ and theestimated signal S(λ)₀. This first estimation can be output at step 720or iterations can be performed. Using the first estimation of the OSNR₀,a second estimation of K, i.e. K₁, is provided in step 710 from (15):

K ₁ =[P _(A)(λ_(p))+P _(B)(λ_(p))][P _(A)(λ_(P))−P_(B)(λ_(p))]·1/(1+1/OSNR ₀).   (20)

In step 510, the noise parameter, i.e. the in-band noise, the OSNR orany other noise parameter, is determined using the determined in-bandnoise level N(λ_(p)) and is typically output.

Steps 712, 714, 716, 718, 720 and 510 are then repeated using a betterestimation of K.

One skilled in the art will appreciate that the method used in step 718to estimate the in-band noise can be varied. Referring to FIG. 8, inExample 1, the in-band noise is evaluated using an interpolation of thevalues of the estimated noise on points c1 and c2, i.e. N(λ_(c1)), andN(λ_(c2)). In another embodiment, all points located in wavelengths inthe neighborhood of c1 and c2 can be used in the interpolation.

In steps 712, 714 and 716 of Example 1, the signal contribution S(λ) isfirst isolated and the noise contribution N(λ) is then discriminated bysubtracting the signal contribution S(λ) from the global opticalspectrum P(λ). It is however noted that the calculations can be varied.For example, the noise contribution N(λ) can be isolated first and thesignal contribution S(λ) discriminated by subtracting the noisecontribution N(λ) from the global optical spectrum P(λ). For example, byassuming N_(A)(λ)=N_(B)(λ) in step 712, equation (16) can be replacedby:

N(λ)=2/(k _(i)−1)·[k _(i) ·P _(A)(λ)−P _(B)(λ)].   (21)

It should further be appreciated that steps 712, 714 and 716 can bemodified to show little sensitivity to unequal noise levels of the twoacquired traces, i.e. when N_(A)(λ)≠N_(B)(λ). In the case of unequalnoise, equations (8) and (9) can be rewritten in a more general form byletting:

N _(B)(λ)=β·N _(A)(λ), that is   (22)

P _(A)(λ)−P _(B)(λ)=S _(A)(λ)−S _(B)(λ)+N _(A)(λ)−N _(B)(λ)=(1−k) S_(A)(λ)+(1−β) N _(A)(λ), and   (23)

P _(A)(λ)+P _(B)(λ)=S _(A)(λ)+S _(B)(λ)+N _(A)(λ)+N _(B)(λ)=(1+k) S_(A)(λ)+(1+β) N _(A)(λ).   (24)

In order to minimize the error introduced by the difference in noiselevel on P_(A)(λ) and P_(B)(λ), a multiplication factor y can be chosen(for example by analysis of the subtraction to avoid discontinuitiesthat can arise when negative values are obtained) and applied toP_(B)(λ) such that (1−·β) is minimized to substantially 0. Thedifference thus obtained is proportional to the signal and we are backin a situation similar to the previously described condition where thenoise was substantially unpolarized.

The OSNR of each signal channel of a DWDM system is obtained byindependently performing the same calculations for each signal peak,using the acquired optical spectrum traces P_(A)(λ) and P_(B)(λ). Nofurther acquisition is required.

As described above, the PPID approach reaches a limit in the specialcase where k is equal to unity, or more generally when k and β are equalwithin the acquisition resolution, therefore leading to identicalsignal-to-noise ratios in the two conditions. In this case, a furtheracquisition with a varied input polarization alignment of the inputoptical signal can be performed to circumvent this undesired condition.The input polarization alignment can be slightly changed by varioussimple means. For example, the input optical fiber 38 can be slightlymoved, or a supplemental piece of hardware, such as the polarizationcontroller 36, can be introduced in the system to change thepolarization alignment of the input optical signal p with respect to thepolarization analysis conditions in such cases.

In one embodiment, a digitally controlled polarization controller 36 isused in a system such as the one shown in FIG. 6. For each in-band noisedetermination to be performed, a first pair of optical spectrum tracesP_(A)(λ), P_(B)(λ) is acquired. The polarization controller 36 is thenused to automatically vary the input conditions, i.e. the polarizationof the input optical signal, and a second pair of optical spectrumtraces P_(C)(λ), P_(D)(λ) is then acquired. The spectrum processor 18then selects which pair of traces to use, or which two traces among thefour to use, for the noise and signal discrimination. The limit casewhere k and β are equal can then be avoided. The selection is made, forexample, by selecting the two traces showing the largest difference insignal peak level from one another. Of course, more than two pairs oftraces may be acquired or multiple analyses can be performed to improvethe accuracy of the method.

In another embodiment, a single channel OSA is used along with the setupof FIG. 4D and multiple acquisitions (more than two) at differentpolarizer conditions are performed. The spectrum processor 18 thenselects which traces to use for the noise and signal discrimination.

Another possibility is to use an alternative algorithm when the approachreaches the limit case where k and β are equal. An example of this is ahybrid approach as described further below in the section entitled“Hybrid Approaches”.

Differential Resolution Bandwidth Discrimination Approach (DRBD)

As discussed hereinabove, a further method of discriminating the opticalnoise contribution from the optical signal contribution in the inputoptical signal is to rely on the differences in their spectralproperties, namely that the noise contribution n(λ) varies more slowlyin wavelength than the signal contribution s(λ). The DifferentialResolution Bandwidth Discrimination (DRBD) approach uses thesedifferential spectral properties between the signal contribution s(λ)and the noise contribution n(λ) in the input optical signal to beanalyzed.

FIG. 9 illustrates a method for determining the in-band noise, the OSNRor any other noise parameter to be determined on the input opticalsignal p using the DRBD approach in general. In step 902, at least twooptical spectrum traces P₁(λ), P₂(λ) of the input optical signal p areobtained using different integration widths. In order to obtain the twotraces, the RBW slit of the OSA can be used to adjust the RBW to twodifferent values. The two traces are then directly acquired using theOSA. The two traces may also be obtained by numerical integration usingsliding windows with different widths, which is the mathematicalequivalent to varying the RBW slit of the OSA. In other words,

P ₁(λ)=h ₁(λ)*h _(OSA1)(λ)*p(λ)=H ₁(λ)*p(λ),   (25)

P ₂(λ)=h ₂(λ)*h _(OSA2)(λ)*p(λ)=H ₂(λ)*p(λ),   (26)

where h_(i)(λ), i=1 or 2, represents the numerical integration functionand h_(OSAi)(λ) represents the OSA integration width caused by hardware,i.e. the RBW slit of the OSA. Either h_(i)(λ) or h_(OSAi)(λ) can bevaried to obtain optical spectrum traces P₁(λ), P₂(λ) with differentintegration widths. The convolution window H_(i)(λ), resultant from thenumerical integration function h_(i)(λ) combined with the OSAintegration width h_(OSAi)(λ), is typically selected such that H₂(λ) islarger than H₁(λ) and H₂(λ) is narrower than the region where the noisecontribution to be determined is present. H₂(λ) is thus typicallynarrower than the channel bandwidth. The resulting traces P₁(λ) andP₂(λ) have different levels of noise and signal contributions and,consequently, different signal-to-noise ratios:

P ₁(λ)=S ₁(λ)+N ₁(λ)=H ₁(λ)*s(λ)+H ₁(λ)*n(λ)=h ₁(λ)*S(λ)+h ₁(λ)*N(λ),  (27)

P ₂(λ)=S ₂(λ)+N ₂(λ)=H ₂(λ)*s(λ)+H ₂(λ)*n(λ)=h ₂(λ)*S(λ)+h ₂(λ)*N(λ).  (28)

As will be discussed in more details below, some assumptions orknowledge on the signal and noise contributions allows for theirdiscrimination.

In step 904, the noise N and signal S contributions are mathematicallydiscriminated using the optical spectrum traces P₁(λ) and P₂(λ).Embodiments of this step are described in more detail below withreference to FIGS. 10 to 12. In step 906, the in-band noise levelN(λ_(p)) is determined from N. In step 908, the noise parameter, i.e.the in-band noise, the OSNR or any other noise parameter, is determinedusing the determined in-band noise level N(λ_(p)) and is typicallyoutput.

EXAMPLE 2

One example embodiment of a method according to the DRBD approach is nowdescribed with reference FIGS. 10 and 11.

In this example, the numerical integration functions h₁(λ) and h₂(λ) arerectangular convolution windows of width RBW₁ and RBW₂ respectively. Instep 1002, the two optical spectrum traces P₁(λ) and P₂(λ) are obtainedby first acquiring an input optical spectrum trace:

P(λ)=h _(OSA)(λ)*p(λ)=S(λ)+N(λ),   (29)

that is then integrated with the convolution windows h₁(λ) and h₂(λ) toobtain P₁(λ) and P₂(λ) respectively (see equations (25) and (26) whereh_(OSA)(λ)=h_(OSM)(λ)=h_(OSA2)(λ) in this case). The two opticalspectrum traces P₁(λ) and P₂(λ) are obtained with h₁(λ) being chosensuch that the resulting signal peak in S₁(λ) is flat, as shown in FIG.11, or the signal peak in S₁(λ) exhibits a constant slope, i.e. in thecase of a non-flat noise, in the immediate region of the peak, i.e. overλ_(p)±δλ, where δλ is greater than the acquisition resolution. In otherwords:

S ₁(λ_(p))=S ₂(λ_(p)), or

h ₁(λ)*S(λ)|λ_(p) =h ₂(λ)*S(λ)|λ_(p),   (30)

where |λ_(p) denotes an evaluation of the preceding expression at λ_(p).

In this example, RBW₂ is larger than RBW₁ and is chosen as the opticalchannel bandwidth, or the defined multiplexer bandwidth for a givennetwork configuration, the multiplexer bandwidth typically correspondingto the bandwidth to which the noise is limited. It is noted that theprecision of the in-noise level to be obtained is a function of thepower level resolution of the acquired optical spectrum traces and ofthe number of data points in the bandwidth defined by RBW₂−RBW₁.

As can be seen on FIG. 11, when both RBW₁ and RBW₂ are selected largerthan the width of the signal peak (the optical signal bandwidth), as thewidth of the convolution window is enlarged from RBW₁ to RBW₂ centeredon the signal peak, the peak level (i.e. at λ_(p)) of the signalcontribution does not substantially vary from S₁(λ) to S₂(λ) in theintegrated optical spectrum traces P₁(λ) and P₂(λ). The variation ofpower level from P₁(λ_(p)) to P₂(λ_(p)) comes from the noisecontribution which is integrated over a larger convolution window inP₂(λ) than in P₁(λ). This variation of the power level at λ_(p) can thenbe used to evaluate the noise contribution at λ_(p), i.e. N(λ_(p)), withthe assumption that the noise in the wavelength range between RBW₁ andRBW₂ is representative of the noise at λ_(p) and then applying thedesired normalizing factor to account for the chosen integration widthsand OSA filter spectral response. Accordingly, in step 1004, the in-bandnoise is estimated by subtracting the two optical spectrum tracesevaluated at λ_(p):

P ₂(λ_(p))−P ₁(λ_(p))=[h ₂(λ)−h ₁(λ)]*N(λ)|λ_(p),   (31)

the in-band noise corresponding to the noise level at the peak centralwavelength λ_(p).

In step 1006, equation (31) is solved to find the in-band noise at λ_(p)(N(λ_(p))) using known mathematical techniques.

In step 1008, the noise parameter, i.e. the in-band noise, the OSNR orany other noise parameter, is determined using the determined in-bandnoise level N(λ_(p)) and is typically output.

This method provides for an adaptive noise estimation technique whereh₂(λ) and h₁(λ) are optimally selected according to the input opticalsignal without requiring an experimented user's judgment.

One will understand that the shape of the convolution windows h₁(λ) andh₂(λ) can be varied. Gaussian shape windows, for example, can be used.

The method of FIG. 10 shows some limitations when the signal isspectrally large and the noise is substantially limited to the opticalsignal bandwidth, forcing the choice of an inappropriate RBW₁ and RBW₂pair and leading to estimation errors in the noise. This, however, canbe remedied when properties of the shapes of the signal contributions(λ) or of the noise contribution n(λ) are known. FIG. 12 illustratessuch a method allowing the determination of an in-band noise parameterwhen shape properties of the signal contribution s(λ) are known.

EXAMPLE 3

When the signal peak is nearly as wide as the width of the noisecontribution in the optical channel, the assumption made in the methodof FIG. 10 that the signal peak power integrated over a wider bandwidthRBW₂ is equal to the signal peak power when integrated over RBW₁ becomesunsuitable. However, when the spectral shape of the signal contributionis known, it is no longer necessary to choose a first convolution windowh₁(λ) that entirely contains all of the signal power. For example, thespectral shape of the signal contribution s(λ) can be found byrecognizing the modulation spectrum of the signal, or relying on thepolarization properties as described above, which would be an example ofa hybrid approach as described below. The spectral shape of the signalcontribution s(λ) can also be found by applying filtering techniquesassuming that the noise contribution n(λ) spectrally varies slowlycompared to the signal contribution s(λ) over the optical signalbandwidth. Spectrally faster components are then identified on theacquired optical spectrum trace and the signal contribution s(λ) isassumed to correspond to those.

Now referring to FIG. 12, in step 1204, the shape of the signalcontribution s(λ) can be either previously known or can be found usingvarious techniques. Assuming that the shape of the signal contributions(λ) is known, the relation factor γ between s₂(λ_(p)) and s₁(λ_(p)) isalso known. Let

γ=s ₂(λ_(p))/s ₁(λ_(p))   (32)

from equations (27) and (28),

P ₂(λ_(p))−γP ₁(λ_(p))=N ₂(λ)−γN ₁(λ)|λ_(p)   (33)

P ₂(λ_(p))−γP ₁(λ_(p))=H ₂(λ)*n(λ)−γH ₁(λ)*n(λ)|λ_(p)   (34)

P ₂(λ_(p))−γP ₁(λ_(p))=[H ₂(λ)−γH1(λ)]*n(λ)|λ_(p).   (35)

In step 1208, since H₂(λ), H₁(λ) and y are known, the noise contributionn(λ) at λ_(p) can be solved for in equation (35) using knownmathematical techniques. For example, if again rectangular convolutionwindows are chosen, step 1208 is equivalent to determining the noisewithin a region corresponding to H₂(λ_(p))−H₁(λ_(p)), and assuming thatthis noise is representative of the noise contribution n(λ) at λ_(p).The in-band noise n(λ_(p)) can be obtained.

In step 1210, the noise parameter, i.e. the in-band noise, the OSNR orany other noise parameter, is determined using the determined in-bandnoise level and is typically output.

It is noted that when determining, in step 1204, the shape of the signalcontribution s(λ) using filtering techniques, it can be difficult toproperly discriminate the noise contribution n(λ) from the signalcontribution s(λ) when the signal contribution has a slowly varyingspectral component that would be filtered when filtering the slowlyvarying noise contribution n(λ). However, proper selection of filtersand evaluation in specific regions of the spectrum, for example on fastrising or falling edges, may allow determination of the shape of thesignal contribution with sufficient precision to identify how the signalcontribution should vary when applying different convolution windows.

It is noted that one skilled in the art will recognize that changes maybe made to the DRBD approach described herein. For example, instead ofperforming numerical convolutions to provide two optical spectrum tracesof the input optical signal, the RBW of the OSA can be varied using itsvariable RBW slit such that the optical spectrum traces acquired withthese new parameters provide the two integration traces. It should stillbe noted that numerical convolutions are less sensitive to thecalibration of the OSA.

Hybrid Approaches

The PPID approach and the DRBD approach each have some differentadvantages and drawbacks and can be combined in a hybrid approach whichcircumvents the limits of each approach. A proper combination of bothapproaches can be chosen for adapting the method to cases where theacquisition conditions limit the performance of one or the other.

For example, as described above, the PPID approach is limited when thesignal-to-noise ratio is the same on the two acquired traces, e.g. whenthe signal level is the same on P_(A)(λ) and P_(B)(λ), andN_(A)(λ)=N_(B)(λ) (i.e. the input optical signal has a polarizationstate that is aligned at 45° with the polarization beam splitter 34 inthe example of FIG. 6). The PPID approach can then be replaced by theDRBD approach.

Furthermore, it should be noted that the DRBD approach is particularlyeffective in cases where the OSNR is small (i.e. when the signal peak issignificantly suppressed). One can benefit from this advantage toimprove the PPID approach in cases where, on one of the acquired trace(P_(A)(λ) or P_(B)(λ)), the signal peak is substantially suppressed.This can be useful when, for example, the shape of the signalcontribution contains artifacts introducing errors in the subtractionprocess. Applying the DRBD to such a trace provides a more accurateestimation of the noise level in that polarization analysis condition.The DRBD approach may also be used in an active polarization nullingmethod (see J. H. Lee et al., “OSNR Monitoring Technique UsingPolarization-Nulling Method”, IEEE Photonics Technology Letters, Vol.13, No. 1, January 2001) to reduce the requirements on the suppressionof the signal. A measurement having a residual signal contribution maythen be used to estimate the noise.

There are multiple ways to combine the two approaches in the manner thatis most suitable for the condition of acquisition that is available. Forexample, it is noted that the PPID approach is dependent on the degreeof polarization (DOP) of the noise and signal contributions. When thepolarization mode dispersion or the polarization dependent loss of thesystem reaches a point where the PPID approach becomes inaccurate, theDRBD approach can be used as an alternative.

EXAMPLE 4

One hybrid approach that combines both the PPID and DRBD approaches inorder to improve their performances is now described with reference toFIG. 13.

In step 1302, two samples p_(A) and p_(B) are produced from the inputoptical signal p using different polarization analysis conditions asdescribed above according to the PPID approach. The two samples p_(A)and p_(B) are typically obtained using a polarization optics arrangementsuch as the polarization beam splitter 34 of FIG. 6.

In step 1304, the optical spectrum traces P_(A)(λ) and P_(B)(λ) of thetwo samples p_(A) and p_(B) are acquired, typically using thedual-channel OSA 31 of FIG. 6. As mentioned previously:

P _(A)(λ)=S _(A)(λ)+N _(A)(λ), and   (36)

P _(B)(λ)=S _(B)(λ)+N _(B)(λ).   (37)

In step 1306, two different integration widths, RBW₁ and RBW₂ (RBW₂larger than RBW₁), are selected such that the signal power issubstantially contained within RBW₁, and RBW₂ is still narrower than thespectral width of the noise contribution. The acquired traces P_(A)(λ)and P_(B)(λ) are then numerically integrated by performing a convolutionrespectively with a rectangular window h₁(λ) and h₂(λ) of width RBW₁ andRBW₂ respectively. This yields the following results:

P _(A1)(λ)=P _(A)(λ)*h ₁(λ)=S _(A1)(λ)+N _(A1)(λ),   (38)

P _(B1)(λ)=P _(B)(λ)*h ₁(λ)=S _(B1)(λ)+N _(B1)(λ),   (39)

P _(A2)(λ)=P _(A)(λ)*h ₂(λ)=S _(A2)(λ)+N _(A2)(λ),   (40)

P _(B2)(λ)=P _(B)(λ)*h ₂(λ)=S _(B2)(λ)+N _(B2)(λ).   (41)

In step 1308, the noise and signal contributions are discriminated byapplying PPID method on the optical spectrum traces P_(A1)(λ),P_(B1)(λ), P_(A2)(λ) and P_(B2)(λ). Let:

K ₁ =[P _(A1)(λ_(p))+P _(B1)(λ_(p))]/[P _(A1)(λ_(p))−P _(B1)(λ_(p))],  (42)

we have:

[P _(A2)(λ_(p))+P _(B2)(λ_(p))]−K ₁ [P _(A2)(λ_(p))−P _(B2)(λ_(p))]=[S_(A2)(λ)+N _(A2)(λ)+S _(B2)(λ)+N _(B2)(λ)]−K ₁ ·[S _(A2)(λ)+N _(A2)(λ)−S_(B2)(λ)−N _(B2)(λ)]  (43)

Then, inserting K₁ from equation (42) into (43), noting thatS_(B1)(λ)=S_(B2)(λ) and S_(A1)(λ)=S_(A2)(λ) as a consequence of thechoice of RBW₁ and RBW₂ in step 1306 above, and assuming thatN_(A1)(λ)=N_(B1)(λ) and N_(A2)(λ)=N_(B2)(λ), which is the case when thenoise contribution is essentially depolarized, then

$\begin{matrix}\begin{matrix}{{\lbrack {{P_{A\; 2}( \lambda_{p} )} + {P_{B\; 2}( \lambda_{p} )}} \rbrack - {K_{1} \cdot \lbrack {{P_{A\; 2}( \lambda_{p} )} - {P_{B\; 2}( \lambda_{p} )}} \rbrack}} = {{N_{A\; 2}(\lambda)} + {N_{B\; 2}(\lambda)} -}} \\{\lbrack {{N_{A\; 1}(\lambda)} + {N_{B\; 1}(\lambda)}} \rbrack} \\{= {N( {\Delta \; R\; B\; W} )}}\end{matrix} & (44)\end{matrix}$

where N(ΔRBW) is the noise contribution integrated in the spectralregion corresponding to RBW₂−RBW₁. N(ERBW) therefore corresponds to thenoise that would be measured by an OSA having a resolution bandwidthequal to the difference of integration widths, i.e. RBW₂−RBW₁.

Other integration widths RBW₁ and RBW₂ can then be used by going back tostep 1306, in order to estimate the noise contribution in otherresolution bandwidths RBW₂−RBW₁, or simply to obtain a more accuratemeasurement of the noise contribution in the same resolution bandwidthRBW₂—RBW₁. Adaptively, other iterations can be performed until it isdetermined that RBW₁ and RBW₂ were properly selected, i.e. RBW₁ and RBW₂are wider than most of the signal power, but still narrower than thenoise contribution width.

In step 1310, the in-band noise level N(λ_(p)) is estimated from N(ΔRBW)obtained in step 1308, assuming that the noise contribution integratedin the spectral region corresponding to RBW₂−RBW₁ is representative ofthe noise contribution at the signal peak λ_(p).

Finally, in step 1312, the noise parameter, i.e. the in-band noise, theOSNR or any other noise parameter, is determined using the estimatedin-band noise level N(λ_(p)) and is typically output.

The embodiments of the invention described above are intended to beexemplary only. The scope of the invention is therefore intended to belimited solely by the scope of the appended claims.

1.-20. (canceled)
 21. A method for determining a noise parameter on aninput optical signal having a data-carrying signal contribution and anoise contribution within an optical signal bandwidth, the methodcomprising: obtaining at least two optical spectrum traces from saidinput optical signal, said optical spectrum traces being taken underdifferent conditions such that they show different non-zerosignal-to-noise ratios; mathematically discriminating said signalcontribution from said noise contribution within said optical signalbandwidth using said optical spectrum traces; determining an in-bandnoise level on said input optical signal from the discriminated noisecontribution; and determining the noise parameter from the determinedin-band noise level, the noise parameter being indicative of the noisecontribution within the optical signal bandwidth.
 22. The method asclaimed in claim 21, wherein the signal contribution and the noisecontribution have mutually different degrees of polarization and whereinsaid obtaining at least two optical spectrum traces comprises: producingat least two samples of the input optical signal, under differentpolarization analysis conditions, states of polarization of the twosamples being arbitrary relative to said input optical signal such thatthey show non-zero signal-to-noise ratios; and acquiring an opticalspectrum of each one of the first and the second samples to obtain saidoptical spectrum traces.
 23. The method as claimed in claim 21, whereinsaid noise contribution within said optical bandwidth varies slowly inwavelength compared to said data-carrying signal contribution, andoptical spectrum traces are obtained using different integration widthsto provide said different conditions, a second one of the integrationwidths being larger than a first one of the integration widths.
 24. Themethod as claimed in claim 23, wherein said obtaining comprises:acquiring an optical spectrum measurement of said input optical signal;convoluting the acquired measurement with a first convolution windowhaving a width corresponding to the first integration width to obtain afirst optical spectrum trace, and convoluting the acquired measurementwith a second convolution window having a width corresponding to thesecond integration width to obtain the second optical spectrum trace.25. The method as claimed in claim 24, wherein said discriminatingcomprises subtracting said optical spectrum traces from one another toobtain a difference trace, a value of said difference trace at a centralwavelength of said optical signal being representative of said opticalnoise.
 26. The method as claimed in claim 21, wherein said noiseparameter comprises an optical signal to noise ratio of the inputoptical signal, determined using the determined in-band noise.
 27. Themethod as claimed in claim 21, further comprising outputting thedetermined noise parameter.
 28. The method as claimed in claim 24,wherein a spectral shape of one of said signal contribution and saidnoise contribution is known within said optical signal bandwidth, saiddiscriminating is performed by assuming said spectral shape.
 29. Amethod for determining a noise parameter on an input optical signalhaving a data-carrying signal contribution and a noise contributionwithin an optical signal bandwidth, said signal contribution beingmostly polarized and said noise contribution being mostly unpolarized,the method comprising: acquiring a first and a second optical spectrumtrace of the input optical signal corresponding to respective first andsecond polarization analysis conditions, said first and secondpolarization analysis conditions being mutually different and arbitraryrelative to said input optical signal such that said optical spectrumtraces show mutually different non-zero signal-to-noise ratios;mathematically discriminating said signal contribution from said noisecontribution within said optical signal bandwidth based on said opticalspectrum traces; and determining said noise parameter from thediscriminated noise contribution, the noise parameter being indicativeof the noise contribution within the optical signal bandwidth.
 30. Themethod as claimed in claim 29, wherein said acquiring a first and asecond optical spectrum trace of the input optical signal comprises:polarization beam splitting said input optical signal into at least twosamples, said samples having mutually orthogonal states of polarization;and acquiring an optical spectrum of each one of said samples to obtainsaid first and second optical spectrum traces.
 31. The method as claimedin claim 30, wherein said discriminating comprises: subtracting saidfirst and second optical spectrum traces from one another to obtain adifference optical spectrum substantially proportional to an opticalspectrum of said signal contribution; estimating an optical spectrum ofsaid signal contribution using said difference optical spectrum;determining an optical spectrum of said input optical signal from atleast one of the first and second optical spectrum traces; anddetermining a level of said optical noise by subtracting the estimatedoptical spectrum of said signal contribution from the determined opticalspectrum of said input optical signal.
 32. The method as claimed inclaim 31, wherein said first and second optical spectrum traces eachhave a signal contribution and a noise contribution and wherein saidperforming calculations further comprises estimating a factor K relatedto a proportion between the signal contributions of said first and saidsecond optical spectrum trace for use in said estimating an opticalspectrum of said input optical signal.
 33. The method as claimed inclaim 29, wherein said acquiring a first and a second optical spectrumtrace of the input optical signal comprises: power splitting said inputoptical signal into a first and a second sample; polarization analyzingsaid first sample and acquiring an optical spectrum of the polarizationanalyzed first sample to obtain said first optical spectrum trace; andacquiring an optical spectrum of said second sample to obtain saidsecond optical spectrum trace, wherein said second sample is notpolarization analyzed.
 34. The method as claimed in claim 29, whereinsaid noise parameter comprises an optical signal to noise ratio of theinput optical signal, determined using the discriminated signal andnoise contributions.
 35. The method as claimed in claim 29, furthercomprising outputting the determined noise parameter.
 36. A system fordetermining a noise parameter on an input optical signal within anoptical signal bandwidth, the system comprising: an input for receivingsaid input optical signal comprising a data-carrying signal contributionand a noise contribution within said optical signal bandwidth, saidsignal contribution and said noise contribution having mutuallydifferent degrees of polarization; a polarization optics arrangement forobtaining a first and a second sample of the input optical signal undermutually different polarization analysis conditions such that at leastone of the first and the second sample is polarization analyzed, a stateof polarization of the at least one polarization analyzed sample beingarbitrary relative to a state of polarization of the input opticalsignal; an optical spectrum analyzer for acquiring a first and a secondoptical spectrum trace respectively of the first and second samples, thefirst and second optical spectrum traces showing mutually differentnon-zero signal to noise ratios; a spectrum processor adapted formathematically discriminating said noise contribution in said inputoptical signal within said optical signal bandwidth based on said firstand second optical spectrum traces; and a noise calculator forevaluating said noise parameter within the optical signal bandwidth fromthe discriminated noise contribution.
 37. The system as claimed in claim36, wherein said spectrum processor comprises a differentiator forcalculating, from said optical spectrum traces, a difference opticalspectrum substantially indicative of said signal contribution; and anoise solver for evaluating said noise contribution using calculationsinvolving said optical spectrum traces and said difference opticalspectrum.
 38. The system as claimed in claim 36, wherein saidpolarization optics arrangement comprise a polarization beam splitterfor splitting said input optical signal into said first and secondsamples, said samples having mutually orthogonal states of polarization.39. The system as claimed in claim 36, further comprising a polarizationdisturbing device for disturbing a state of polarization of said inputoptical signal to vary the signal to noise ratio on at least one of saidoptical spectrum traces such that the first and second optical spectrumtraces show different signal to noise ratios.
 40. The systems as claimedin claim 36, wherein said noise parameter comprises an optical signal tonoise ratio of the input optical signal, determined using thediscriminated signal and noise contributions.